5,693 research outputs found

    CANCER TREATMENT BY TARGETING HDAC4 TRANSLOCATION INDUCED BY MICROSECOND PULSED ELECTRIC FIELD EXPOSURE: MECHANISTIC INSIGHTS THROUGH KINASES AND PHOSPHATASES

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    Epigenetic modifications, arising from sub-cellular shifts in histone deacetylase (HDAC) activity and localization, present promising strategies for diverse cancer treatments. HDACs, enzymes responsible for post-translational histone modifications, induce these epigenetic changes by removing acetyl groups from Δ-N-acetyl-lysine residues on histones, thereby suppressing gene transcription. Within the HDAC group, class IIa HDACs are notable for their responsiveness to extracellular signals, bridging the gap between external stimuli, plasma membrane, and genome through nuclear-cytoplasmic translocation. This localization offers two significant mechanisms for cancer treatment: nuclear accumulation of HDACs represses oncogenic transcription factors, such as myocyte-specific enhancer factor 2C (MEF2C), triggering various cell death pathways. Conversely, cytoplasmic HDAC accumulation acts similarly to HDAC inhibitors by silencing genes. My dissertation introduces an innovative approach for glioblastoma and breast cancer treatment by investigating the application of microsecond pulsed electric fields. It particularly focuses on HDAC4, a class IIa HDAC overexpressed in these cancers. Beyond demonstrating HDAC4 translocation, my research delves into the intricate roles of kinases and phosphatases, shedding light on the underlying factors governing HDAC4 translocation

    Irish Ocean Climate and Ecosystem Status Report

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    Summary report for Irish Ocean Climate & Ecosystem Status Report also published here. This Irish Ocean Climate & Ecosystem Status Summary for Policymakers brings together the latest evidence of ocean change in Irish waters. The report is intended to summarise the current trends in atmospheric patterns, ocean warming, sea level rise, ocean acidification, plankton and fish distributions and abundance, and seabird population trends. The report represents a collaboration between marine researchers within the Marine Institute and others based in Ireland’s higher education institutes and public bodies. It includes authors from Met Éireann, Maynooth University, the University of Galway, the Atlantic Technological University, National Parks and Wildlife, Birdwatch Ireland, Trinity College Dublin, University College Dublin, Inland Fisheries Ireland, The National Water Forum, the Environmental Protection Agency, and the Dundalk Institute of Technology.This report is intended to summarise the current trends in Ireland’s ocean climate. Use has been made of archived marine data held by a range of organisations to elucidate some of the key trends observed in phenomena such as atmospheric changes, ocean warming, sea level rise, acidification, plankton and fish distributions and abundance, and seabirds. The report aims to summarise the key findings and recommendations in each of these areas as a guide to climate adaptation policy and for the public. It builds on the previous Ocean Climate & Ecosystem Status Report published in 2010. The report examines the recently published literature in each of the topic areas and combines this in many cases with analysis of new data sets including long-term time series to identify trends in essential ocean variables in Irish waters. In some cases, model projections of the likely future state of the atmosphere and ocean are presented under different climate emission scenarios.Marine Institut

    ACC Saturator: Automatic Kernel Optimization for Directive-Based GPU Code

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    Automatic code optimization is a complex process that typically involves the application of multiple discrete algorithms that modify the program structure irreversibly. However, the design of these algorithms is often monolithic, and they require repetitive implementation to perform similar analyses due to the lack of cooperation. To address this issue, modern optimization techniques, such as equality saturation, allow for exhaustive term rewriting at various levels of inputs, thereby simplifying compiler design. In this paper, we propose equality saturation to optimize sequential codes utilized in directive-based programming for GPUs. Our approach simultaneously realizes less computation, less memory access, and high memory throughput. Our fully-automated framework constructs single-assignment forms from inputs to be entirely rewritten while keeping dependencies and extracts optimal cases. Through practical benchmarks, we demonstrate a significant performance improvement on several compilers. Furthermore, we highlight the advantages of computational reordering and emphasize the significance of memory-access order for modern GPUs

    Yet another Improvement of Plantard Arithmetic for Faster Kyber on Low-end 32-bit IoT Devices

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    This paper presents another improved version of Plantard arithmetic that could speed up Kyber implementations on two low-end 32-bit IoT platforms (ARM Cortex-M3 and RISC-V) without SIMD extensions. Specifically, we further enlarge the input range of the Plantard arithmetic without modifying its computation steps. After tailoring the Plantard arithmetic for Kyber's modulus, we show that the input range of the Plantard multiplication by a constant is at least 2.45 times larger than the original design in TCHES2022. Then, two optimization techniques for efficient Plantard arithmetic on Cortex-M3 and RISC-V are presented. We show that the Plantard arithmetic supersedes both Montgomery and Barrett arithmetic on low-end 32-bit platforms. With the enlarged input range and the efficient implementation of the Plantard arithmetic on these platforms, we propose various optimization strategies for NTT/INTT. We minimize or entirely eliminate the modular reduction of coefficients in NTT/INTT by taking advantage of the larger input range of the proposed Plantard arithmetic on low-end 32-bit platforms. Furthermore, we propose two memory optimization strategies that reduce 23.50% to 28.31% stack usage for the speed-version Kyber implementation when compared to its counterpart on Cortex-M4. The proposed optimizations make the speed-version implementation more feasible on low-end IoT devices. Thanks to the aforementioned optimizations, our NTT/INTT implementation shows considerable speedups compared to the state-of-the-art work. Overall, we demonstrate the applicability of the speed-version Kyber implementation on memory-constrained IoT platforms and set new speed records for Kyber on these platforms

    Deployment of Deep Neural Networks on Dedicated Hardware Accelerators

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    Deep Neural Networks (DNNs) have established themselves as powerful tools for a wide range of complex tasks, for example computer vision or natural language processing. DNNs are notoriously demanding on compute resources and as a result, dedicated hardware accelerators for all use cases are developed. Different accelerators provide solutions from hyper scaling cloud environments for the training of DNNs to inference devices in embedded systems. They implement intrinsics for complex operations directly in hardware. A common example are intrinsics for matrix multiplication. However, there exists a gap between the ecosystems of applications for deep learning practitioners and hardware accelerators. HowDNNs can efficiently utilize the specialized hardware intrinsics is still mainly defined by human hardware and software experts. Methods to automatically utilize hardware intrinsics in DNN operators are a subject of active research. Existing literature often works with transformationdriven approaches, which aim to establish a sequence of program rewrites and data-layout transformations such that the hardware intrinsic can be used to compute the operator. However, the complexity this of task has not yet been explored, especially for less frequently used operators like Capsule Routing. And not only the implementation of DNN operators with intrinsics is challenging, also their optimization on the target device is difficult. Hardware-in-the-loop tools are often used for this problem. They use latency measurements of implementations candidates to find the fastest one. However, specialized accelerators can have memory and programming limitations, so that not every arithmetically correct implementation is a valid program for the accelerator. These invalid implementations can lead to unnecessary long the optimization time. This work investigates the complexity of transformation-driven processes to automatically embed hardware intrinsics into DNN operators. It is explored with a custom, graph-based intermediate representation (IR). While operators like Fully Connected Layers can be handled with reasonable effort, increasing operator complexity or advanced data-layout transformation can lead to scaling issues. Building on these insights, this work proposes a novel method to embed hardware intrinsics into DNN operators. It is based on a dataflow analysis. The dataflow embedding method allows the exploration of how intrinsics and operators match without explicit transformations. From the results it can derive the data layout and program structure necessary to compute the operator with the intrinsic. A prototype implementation for a dedicated hardware accelerator demonstrates state-of-the art performance for a wide range of convolutions, while being agnostic to the data layout. For some operators in the benchmark, the presented method can also generate alternative implementation strategies to improve hardware utilization, resulting in a geo-mean speed-up of ×2.813 while reducing the memory footprint. Lastly, by curating the initial set of possible implementations for the hardware-in-the-loop optimization, the median timeto- solution is reduced by a factor of ×2.40. At the same time, the possibility to have prolonged searches due a bad initial set of implementations is reduced, improving the optimization’s robustness by ×2.35

    Elements of Ion Linear Accelerators, Calm in The Resonances, Other_Tales

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    The main part of this book, Elements of Linear Accelerators, outlines in Part 1 a framework for non-relativistic linear accelerator focusing and accelerating channel design, simulation, optimization and analysis where space charge is an important factor. Part 1 is the most important part of the book; grasping the framework is essential to fully understand and appreciate the elements within it, and the myriad application details of the following Parts. The treatment concentrates on all linacs, large or small, intended for high-intensity, very low beam loss, factory-type application. The Radio-Frequency-Quadrupole (RFQ) is especially developed as a representative and the most complicated linac form (from dc to bunched and accelerated beam), extending to practical design of long, high energy linacs, including space charge resonances and beam halo formation, and some challenges for future work. Also a practical method is presented for designing Alternating-Phase- Focused (APF) linacs with long sequences and high energy gain. Full open-source software is available. The following part, Calm in the Resonances and Other Tales, contains eyewitness accounts of nearly 60 years of participation in accelerator technology. (September 2023) The LINACS codes are released at no cost and, as always,with fully open-source coding. (p.2 & Ch 19.10)Comment: 652 pages. Some hundreds of figures - all images, there is no data in the figures. (September 2023) The LINACS codes are released at no cost and, as always,with fully open-source coding. (p.2 & Ch 19.10

    Evolutionary genomics of cowpox virus and recombination in vitro between a naturally occurring cowpox virus and a vaccinia virus vectored influenza vaccine

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    Modified vaccinia virus Ankara (MVA) is a promising orthopoxvirus (OPXV) vector vaccine candidate due to its host range restriction and good safety profile as a smallpox vaccine. It has been widely tested in clinical trials as a recombinant vector for vaccination against infectious diseases and cancers in humans and animals. Furthermore, it is being used as a smallpox and Mpox vaccine. However, the extensive use of MVA and MVA vectored vaccines have the potential for MVA or MVA vectored vaccine to recombine with naturally circulating OPXV. Cowpox virus (CPXV) as a close relative of MVA is a potential candidate for recombination. Hence, the genetic diversity and evolution of CPXV was assessed in this work, as well as recombination in vitro between a naturally occurring CPXV and MVA vectored vaccine in cells in which MVA multiplies poorly. CPXV is classified as a single species; however, we demonstrated that CPXV might be an assemblage of several species based on its high genetic diversity, lack of monophyly, and close phylogenetic relationship with other OPXV. CPXV strains were separated into five major clusters rather than one monophyletic cluster. Furthermore, we described a new, distinct cluster closely related to Ectromelia virus (ECTV) and Abatino macacapox virus (Abatino) named “ECTV-Abatino-like CPXV”. Additionally, we showed evidence that a Norwegian CPXV isolate was a natural occurring recombinant CPXV that might have emerged following multiple recombination events between different OPXV species from the Old World and North America. Under in vitro conditions, the progeny viruses obtained from co-infection and superinfection of Vero cells with MVA-HANP and CPXV-No-F1 had mosaic genomes and displayed parental and non-parental plaque phenotypes. Furthermore, some progeny viruses contained the transgene from MVA-HANP and regained genes that were deleted or fragmented in MVA-HANP. Overall, these findings will contribute to the environmental risk assessment of MVA and MVA vectored vaccines and to the improvement of the biosafety of MVA vectored vaccines

    Signal Processing Techniques for Radar Cross Section Measurements Using Orthogonal Frequency-Division Multiplexing Waveforms

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    In recent work conducted at the University of Oklahoma’s Advanced Radar Research Center, it has been shown that using orthogonal frequency-division multiplexing (OFDM) offers a significant time reduction in taking wideband radar cross section (RCS) measurements, compared with traditional techniques. This has led to an interest as to whether or not the reduced measurement time enables wideband RCS measurements of moving targets. In an attempt to answer this question, this thesis presents a simulation framework for RCS extraction of a moving target. Because the target is moving, it is assumed that measurements are taken in an outdoor environment. As such, ground clutter is the primary competing interference. It is shown that in order to recover the target RCS, range-Doppler filtering must be performed. As a result, the filter shape, available Doppler resolution, and signal-to-noise ratio become the primary determiners of performance. Some closed-form expressions are derived to help determine acceptable system parameters and improve performance. Interfering signals from other transmitters are also considered in this work. It is shown that if an interfering signal corrupts part of the spectrum, filtering is impossible because the target cannot be located in the range-Doppler space. To combat this, the spectrum is nulled at points where interference occurs. This enables filtering to be applied; however, nulls will exist in the RCS measurement. Finally, some spectral reconstruction techniques are discussed and tested with the purpose of estimating pieces of the spectrum that were lost
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